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Peer-review systems such as SWoRD lack intelligence for detecting and responding to problems with students’ reviewing performance. While prior work has demonstrated the feasibility of automatically identifying desirable feedback features in free-text reviews of student papers, similar methods have not yet been developed for feedback regarding argument diagrams. One desirable feedback feature is problem localization, which has been shown to positively correlate with feedback implementation in both student papers and argument diagrams. In this paper we demonstrate that features previously developed for identifying localization in paper reviews do not work well when applied to peer reviews of argument diagrams. We develop a novel algorithm tailored for reviews of argument diagrams, and demonstrate significant performance improvements in identifying problem localization in an experimental evaluation.

It is common that students peer-review other students' writing, and these reviews are useful information to instructors, both on the particulars of the essay being reviewed, the feedback provided and the overall progress of the class. This paper describes a novel approach to summarising feedback in academic essay writing. We present a summarisation method for identifying and extracting representative opinion sentences from each feedback. Sentiment score-based techniques are employed and SentiWordNet is used as a linguistic lexical resource for sentiment summarisation. We evaluate our approach with the reviews written by a group of 50 engineering students.

In this paper, we use supervised machine learning to automatically identify the problem localization of peer-review feedback Using five features extracted via Natural Language Processing techniques, the learned model significantly outperforms a standard baseline Our work suggests that it is feasible for future tutoring systems to generate assessments regarding the use of localization in student peer reviews.

In the context of Intelligent Tutoring Systems, there is a potential for adapting either content or its sequence to student as to enhance the learning experience. Recent theories propose the use of team-working environments to improve even further this experience. In this paper an effective matching algorithm is presented in the context of peer reviewing applied to an educational setting. The problem is formulated as an optimization problem to search a solution that satisfies a set of given criteria modeled as "profiles". These profiles represent regions of the solution space to be either favored or avoided when searching for a solution. The proposed technique was deployed in a first semester computer engineering course and proved to be both effective and well received by the students.

In this survey talk we discuss several problems related to peer to peer networks. A host of issues arises in the context of peer to peer networks, including efficiency issues, censorship issues, anonymity issues, etc. While many of these problems have been studied in the past, the file swapping application has taken over the Internet, given these problems renewed impetus. I will discuss papers co-authored with J. Saia, E. Cohen, H. Kaplan, R. Berman, A. Ta-Sham, and others.

Despite the fact the hypermedia has long been discussed as a suitable format for academic research publications, the academic community has made little progress toward realizing hypermedia publishing. In this paper, we provide an initial framework for considering the problems facing any system designed for publishing academic hypertexts, and propose some first steps our community can take toward realizing this goal.